Abstract. The aggregate behaviors of students in a learning environment can collectively encode information about the pedagogical objects with which they interact. In this presentation, I will demonstrate ways in which the synthesis of data from higher-ed can illuminate the terrain of the university and support students in their decision making and wayfinding. A novel application of recurrent neural networks and skip-grams, techniques popularized by their application to modeling language, are brought to bear on millions of historic student course enrollments to create vector representations of these objects. Analysis of the produced vector space reveals predictive information about students’ on-time graduation and a tremendous degree of semantic relational information about courses which can be visualized, reasoned about, and surfaced to students. Our course information platform, adopted by the UCB Office of the Registrar and two other public institutions, uses this automatically inferred semantic information to help students navigate the university’s offerings and provides personalized course suggestions based on topic preference, course history, and program requirements.

SPEAKER: Zachary Pardos, Berkely School of Information and Graduate School of Education

Dr. Pardos is an Assistant Professor at UC Berkeley in the School of Information and Graduate School of Education. His focal areas of study are knowledge representation and personalized supports leveraging big data in education. He earned his PhD in Computer Science at WPI and comes to UC Berkeley after a post-doc at MIT Computer Science Artificial Intelligence Lab (CSAIL). At UC Berkeley he directs the Computational Approaches to Human Learning (CAHL) research lab and teaches courses on data mining and analytics, digital learning environments, and machine learning in education.